A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities,and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation.However,this method has some problems,such as relying on expert experience and poor portability.Inspired by the rule-based entity relation extraction method,this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed,which is abbreviated as RSTAC.This model refines the extraction rules of relation semantic templates from relation corpus through dependency parsing and realizes the automatic construction of relation semantic templates.Based on the relation semantic template,the process of relation classification and triplet extraction is constrained,and finally,the entity relation triplet is obtained.The experimental results on the three major Chinese datasets of DuIE,SanWen,and FinRE showthat the RSTAC model successfully obtains rich deep semantics of relation,improves the extraction effect of entity relation triples,and the F1 scores are increased by an average of 0.96% compared with classical joint extraction models such as CasRel,TPLinker,and RFBFN.

Word sense disambiguation using semantic relatedness measurement

All human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is intended in a given context” is known as “word sense disambiguation (WSD)”. This paper presents a method of WSD that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to “literally” and “regularly” express a “concept”. We apply set algebra to WordNet’s synsets cooperating with WordNet’s word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in WordNet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts—we facilitate “concept distribution statistics” to determine the degree of semantic relatedness between two lexically expressed con- cepts. The experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for WSD.

On Maintenance of Inter-connectivity Among Multi-representations

As the problems of conceptual and representational differences will arise among multi-representations, in- ter-connectivity maintenance among multi-representations exists as a foundational task in building multi-scale data model. Since the existing methods are still not satisfactory in practice, the inter-connectivity among multiple representa- tions can be only achieved if the multi-scale model is capable of explicitly inter-relating them and dealing with their differences. So, this paper firstly explores the relation among multiple representations from the same entity, such as multi-semantic, multi-geometry, multi-attributes, hierarchical semantic relations and so on. Based on these, this paper proposes aggregation-based semantic hierarchical matching rules (ASHMR) as the basis of tackling inter-connectivity among multi-representations, and defines the available hierarchical semantic knowledge, namely semantically equal, semantically related and semantically irrelevant. According to different change among multi-representations from dif- ferent types of objects, the applications and techniques of the corresponding hierarchy inter-connectivity matching crite- rion are explored. And taken the road intersections as examples, a case in point is given in details for describing the strategies of inter-connectivity maintenance, showing that this method is feasible to deal with inter-connectivity.

Semantic Features and Applications in Translation of English Words in Pairs

English words in pairs are a special form of English idioms, which have different kinds and are used widely. For English learners, words in pairs are one of the difficult points. This paper discusses their form patterns, semantic relations, grammatical functions, rhetoric features and their application in translation. Its purpose is to help learners understand and use them accurately and correctly so as to improve language expressing ability.

A Study on Semantic Relations of“and”Used in Chinese EFL Learners’Narrative Writing

Studies on conjunctions used by Chinese English as a Foreign Language(EFL)learners over the past ten years have focused mainly on the use of conjunctions in argumentative writing,and there is little empirical work on conjunction“and”in narrative writing.The purpose of this paper is to explore the characteristics of the semantic relations of“and”used in the narrative writing of Chinese EFL learners from the perspective of text coherence.Through analysis of narrative writing of 29 sophomores,this study investigates the characteristics of semantic relations expressed by the conjunction“and”and the differences in the use of semantic relations of“and”between high-score and low-score writing.The results show different frequencies of the use of semantic relations of“and”.ELF learners prefer to use the term“and”to build progressive relation and parallel relation more than any other relation.Both high-score and low-score writing use a sizable number of“and”to build progressive relation and parallel relation,but high-score writing obviously contains more guiding relations and fewer supplementary relations.These findings have some pedagogical implications for teaching transitions.

Kernel-Based Semantic Relation Detection and Classification via Enriched Parse Tree Structure

This paper proposes a tree kernel method of semantic relation detection and classification （RDC） between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.

Graph reasoning over explicit semantic relation

Multi-hop reasoning over language or graphs represents a significant challenge in contemporary research,particularly with the reliance on deep neural networks.These networks are integral to text reasoning processes,yet they present challenges in extracting and representing domain or commonsense knowledge,and they often lack robust logical reasoning capabilities.To address these issues,we introduce an innovative text reasoning framework.This framework is grounded in the use of a semantic relation graph and a graph neural network,designed to enhance the model’s ability to encapsulate knowledge and facilitate complex multi-hop reasoning.Our framework operates by extracting knowledge from a broad range of texts.It constructs a semantic relationship graph based on the logical relationships inherent in the reasoning process.Beginning with the core question,the framework methodically deduces key knowledge,using it as a guide to iteratively establish a complete evidence chain,thereby determining the final answer.Leveraging the advanced reasoning capabilities of the graph neural network,this approach is adept at multi-hop logical reasoning.It demonstrates strong performance in tasks like machine reading comprehension and question answering,while also clearly delineating the path of logical reasoning.